Bibliographic record
Abstract
While immigration and immigrant health have received widespread attention in the social science and health literature, the phenomenon of non-migrant mobility - changing residence within a neighborhood or city - is less well-studied. This research examines interview findings in the context of available socioeconomic data to generate hypotheses about the relationship between mobility and health in the Hamilton neighborhood of Beasley. Since the period of Hamilton's industrial expansion in the late l800s, Beasley neighborhood has served as a landing point for new immigrants to Canada. While immigration remains a source of Beasley's high mobility rates, nonmigrant mobility (within census tract) accounts for a significant proportion of mobility within Beasley. The socioeconomic circumstances surrounding immigrant and nonmigrant moves are dissimilar. Immigrants to Canada are motivated by ''pull'' factors such as economic and educational opportunity, and increased access to health services for their families. Non-migrant movers are "pushed" to move by economic instability and a lack of affordable, quality housing. In addition, the health effects of mobility differ for immigrant and non-migrant movers. While existing studies suggest that immigrant health improves upon arrival (Hyman 2001), the health of non-migrant movers may be compromised by their mobility status. The thesis generates hypotheses for the study of urban mobility and concludes with methodological suggestions for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".